Does product innovation mediate the relationship between marketing innovation and innovative performance in manufacturing companies?
Bibliographic record
Abstract
Innovation in the manufacturing industry is viewed as crucial due to its substantial effects on performance. This view has led researchers to evaluate the importance of different types of innovation within manufacturing companies. The influence of marketing innovation on product innovation and overall innovative performance is examined in the present study. The study also aims to explore the influence of product innovation on innovative performance and to analyze the mediating role of product innovation in the relationship between marketing innovation and innovative performance. Questionnaires were distributed to 384 managers from Palestinian manufacturing firms through convenience sampling. Structural equation modelling was employed as the data analysis tool. According to the study's findings, marketing innovation directly and positively impacts both product innovation and innovative performance, while product innovation positively influences innovative performance. Additionally, product innovation partially mediates the relationship between marketing innovation and innovative performance. This study is different from previous research as it focuses on the interrelationships between various dimensions of firms' innovation and performance. It adds to the literature on manufacturing performance by further validating the scales of innovation and performance. This approach could offer new insights into existing models of innovation and performance, crucial for success, by examining the interrelationships among organizational innovation dimensions, specifically marketing innovation, product innovation, and innovative performance, within the Palestinian manufacturing sector, which operates in a developing country facing conflict.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".